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Demand forecasting

Balance overstocks and out-of-stocks with DSLab's demand forecasting
Demand forecasting is a barebone of every retailer's business. Order too much, and you accumulate overstock; order too little, and you lose sales and customers. Machine learning, however, gives a fresh perspective on how to strike a balance and predict demand as accurately as possible. Get an automated, highly accurate forecast for every SKU on a store level with DSLab's demand forecasting.

Increase on-shelf availability while reducing write-offs

demand forecasting
Granular forecasts
We deliver highly accurate daily forecasts: for each item, on a store level, for 16 weeks ahead.
machine learning for demand forecasting
Machine learning
Machine learning technologies at the core of our solution deliver highly-accurate forecasts allowing you to improve on-shelf availability without excessive stock.
parameters of demand forecasting
Hundreds of parameters
While classical rule-based systems analyze a maximum of 50 parameters, our predictive models account for over 600 parameters.
automation of the demand forecasting process
Our solution allows you to eliminate highly intensive routine tasks, avoid human errors, and fully automate the forecasting process.
business metrics
Business metrics
Instead of focusing on accuracy only, we use business metrics such as write-offs and out-of-stocks to measure the quality of the forecast.
easy integration
Easy integration
DSLab forecasting solution is easily integrated with standard data sources such as Hadoop or SQL-based data storage, as well as replenishment systems.

Potential business effect

Get measurable business value
with DSLab's fresh food forecasting
decrease in write-offs
reduction of out-of-stocks
Contact us to learn more about ML-based demand forecasting and how this can benefit your business
Contact us
Get in touch to see how AI can benefit your business
By submitting this form I give my consent to DSLab to process my personal data in accordance with privacy policy

More about forecasting in retail

Here are some resources you may find helpful
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